
@article{data2020,
  title = {Replication Data for: Achieving Statistical Significance with Control Variables and without Transparency},
  author = {Lenz, Gabriel S. and Sahn, Alexander},
  year = {2020},
  journal = {Harvard Dataverse},
  volume = {V1},
  url = {https://doi.org/10.7910/DVN/XIEJCR},
}


@article{achen_new_2002a,
  title = {Toward a New Political Methodology: {{Microfoundations}} and {{ART}}},
  author = {Achen, C. H.},
  year = {2002},
  volume = {5},
  pages = {423--450},
  abstract = {The past two decades have brought revolutionary change to the field of political methodology. Steady gains in theoretical sophistication have combined with explosive increases in computing power to produce a profusion of new estimators for applied political researchers. Attendance at the annual Summer Meeting of the Methodology Section has multiplied many times, and section membership is among the largest in APSA. All these are signs of success. Yet there are warning signs, too. This paper attempts to critically summarize current developments in the young field of political methodology. It focuses on recent generalizations of dichotomous-dependent-variable estimators such as logit and probit, arguing that even our best new work needs a firmer connection to credible models of human behavior and deeper foundations in reliable empirical generalizations.},
  journal = {Annual Review of Political Science}
}

@article{altonji_evaluation_2005,
  title = {An Evaluation of Instrumental Variable Strategies for Estimating the Effects of Catholic Schooling},
  author = {Altonji, Joseph G. and Elder, Todd E. and Taber, Christopher R.},
  year = {2005},
  volume = {40},
  pages = {791--821},
  issn = {0022-166X},
  abstract = {Several previous studies have relied on religious affiliation and the proximity to Catholic schools as exogenous sources of variation for identifying the effect of Catholic schooling on a wide variety of outcomes. Using three separate approaches, we examine the validity of these instrumental variables. We find that none of the candidate instruments is a useful source of identification in currently available data sets. We also investigate the role of exclusion restrictions versus nonlinearity as the source of identification in bivariate probit models. The analysis may be useful as a template for the assessment of instrumental variables strategies in other applications.},
  journal = {The Journal of Human Resources},
  number = {4}
}

@book{angrist_mostly_2009,
  title = {Mostly Harmless Econometrics: An Empiricist's Companion},
  author = {Angrist, Joshua David and Pischke, J{\`e}orn-Steffen},
  year = {2009},
  publisher = {{Princeton University Press}},
  address = {{Princeton}},
  keywords = {Econometrics.,Regression analysis.}
}

@article{athey_efficient_2016,
  title = {Efficient {{Inference}} of {{Average Treatment Effects}} in {{High Dimensions}} via {{Approximate Residual Balancing}}},
  author = {Athey, Susan and Imbens, Guido W. and Wager, Stefan},
  year = {2018},
  volume = {80},
  pages = {597--623},
  abstract = {There are many studies where researchers are interested in estimating average treatment effects and are willing to rely on the unconfoundedness assumption, which requires that treatment assignment is as good as random conditional on pre-treatment variables. The unconfoundedness assumption is often more plausible if a large number of pre-treatment variables are included in the analysis, but this can worsen the finite sample properties of existing approaches to estimation. In particular, existing methods do not handle well the case where the model for the propensity score (that is, the model relating pre-treatment variables to treatment assignment) is not sparse. In this paper, we propose a new method for estimating average treatment effects in high dimensions that combines balancing weights and regression adjustments. We show that our estimator achieves the semi-parametric efficiency bound for estimating average treatment effects without requiring any modeling assumptions on the propensity score. The result relies on two key assumptions, namely overlap (that is, all units have a propensity score that is bounded away from 0 and 1), and sparsity of the model relating pre-treatment variables to outcomes.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\3SCZ47VE\\3408.html;C\:\\gl\\Box Sync\\zotero\\storage\\CVGMTTN2\\3408.html;C\:\\gl\\Box Sync\\zotero\\storage\\PC8FPZ9H\\3408.html},
  journal = {Journal of the Royal Statistical Society-Series B},
  number = {4}
}

@article{athey_measure_2015,
  title = {A {{Measure}} of {{Robustness}} to {{Misspecification}}},
  author = {Athey, Susan and Imbens, Guido},
  year = {2015},
  month = may,
  volume = {105},
  pages = {476--480},
  doi = {10.1257/aer.p20151020},
  abstract = {approach to assessing sensitivity to specification. We construct estimates of the object of interest for each of a large set of models. Our proposed robustness measure is the standard deviation of the point estimates over the set of models. Each member of the set is generated by splitting the sample into two subsamples based on covariate values, constructing separate parameter estimates for each subsample, and then combining the results.},
  journal = {The American Economic Review},
  number = {5}
}

@article{bartels_specification_1997,
  title = {Specification Uncertainty and Model Averaging},
  author = {Bartels, Larry M.},
  year = {1997},
  month = apr,
  volume = {41},
  pages = {641--674},
  abstract = {Theory: Data analysts sometimes report (and more often produce) results from many alternative models with different explanatory variables, functional forms, observations, or exogeneity assumptions. Classical statistical theory is ill-suited to make sense of this practice. Hypotheses: Bayesian statisticians have recently proposed a coherent procedure for taking account of specification uncertainty by averaging results from a variety of different model specifications. The model-averaging procedure has the general effect of discounting evidence derived from elaborate specification searches, especially when alternative models produce markedly different results. Methods: I describe the model-averaging procedure, and illustrate its application using examples drawn from a controversy in comparative political economy between Lange and Garrett (1985, 1987) and Jackman (1987), and from the work of Erikson, Wright, and McIver (1993) on public opinion and policy in the American states. In addition, I propose two classes of reference priors that might usefully supplement the uniform model priors typically adopted in model averaging-a ''dummy-resistant prior'' for dealing with outlier observations, and a family of ''search-resistant priors'' for representing sequential specification searches. Results: The model-averaging procedure seems to offer a convenient approximation to full-blown Bayesian analysis in typical social science settings. It is simple to implement, and uses the variety of alternative model specifications already being produced by data analysts to shed some useful light on the inferential implications of specification uncertainty.},
  journal = {American Journal of Political Science}
}

@article{beckstead_isolating_2012,
  title = {Isolating and Examining Sources of Suppression and Multicollinearity in Multiple Linear Regression},
  author = {Beckstead, Jason W.},
  year = {2012},
  month = mar,
  volume = {47},
  pages = {224--246},
  issn = {0027-3171},
  doi = {10.1080/00273171.2012.658331},
  abstract = {The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic strategy to isolate, examine, and remove suppression effects has been offered. In this article such an approach, rooted in confirmatory factor analysis theory and employing matrix algebra, is developed. Suppression is viewed as the result of criterion-irrelevant variance operating among predictors. Decomposition of predictor variables into criterion-relevant and criterion-irrelevant components using structural equation modeling permits derivation of regression weights with the effects of criterion-irrelevant variance omitted. Three examples with data from applied research are used to illustrate the approach: the first assesses child and parent characteristics to explain why some parents of children with obsessive-compulsive disorder accommodate their child's compulsions more so than do others, the second examines various dimensions of personal health to explain individual differences in global quality of life among patients following heart surgery, and the third deals with quantifying the relative importance of various aptitudes for explaining academic performance in a sample of nursing students. The approach is offered as an analytic tool for investigators interested in understanding predictor-criterion relationships when complex patterns of intercorrelation among predictors are present and is shown to augment dominance analysis.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\FB4GUV5I\\Beckstead - 2012 - Isolating and Examining Sources of Suppression and.pdf;C\:\\gl\\Box Sync\\zotero\\storage\\IHPFTE5T\\00273171.2012.html},
  journal = {Multivariate Behavioral Research},
  number = {2},
  pmid = {26734849}
}

@article{bhattacharya_instrumental_2012,
  title = {Do Instrumental Variables Belong in Propensity Scores?},
  author = {Bhattacharya, Jay and Vogt, William B},
  year = {2012},
  volume = {9},
  pages = {107--127},
  issn = {0975-556X},
  journal = {International Journal of Statistics \& Economics},
  number = {A12}
}

@book{bobko_correlation_2001,
  title = {Correlation and Regression: Applications for Industrial Organizational Psychology and Management},
  shorttitle = {Correlation and Regression},
  author = {Bobko, Philip},
  year = {2001},
  month = apr,
  publisher = {{SAGE}},
  abstract = {". . . the writing makes this book interesting to all levels of students. Bobko tackles tough issues in an easy way but provides references for more complex and complete treatment of the subject. . . . there is a familiarity and love of the material that radiates through the words." --Malcolm James Ree, ORGANIZATIONAL RESEARCH METHODS, April 2002 "This book provides one of the clearest treatments of correlations and regression of any statistics book I have seen. . . . Bobko has achieved his objective of making the topics of correlation and regression accessible to students. . . . For someone looking for a very clearly written treatment of applied correlation and regression, this book would be an excellent choice." --Paul E. Spector, University of South Florida "As a quantitative methods instructor, I have reviewed and used many statistical textbooks. This textbook and approach is one of the very best when it comes to user-friendliness, approachability, clarity, and practical utility." --Steven G. Rogelberg, Bowling Green State University Building on the classical examples in the first edition, this updated edition provides students with an accessible textbook on statistical theories in correlation and regression. Taking an applied approach, the author uses concrete examples to help the student thoroughly understand how statistical techniques work and how to creatively apply them based on specific circumstances they face in the "real world." The author uses a layered approach in each chapter, first offering the student an intuitive understanding of the problems or examples and progressing through to the underlying statistics. This layered approach and the applied examples provide students with the foundation and reasoning behind each technique, so they will be able to use their own judgement to effectively choose from the alternative data analytic options.},
  googlebooks = {VkY5DQAAQBAJ},
  isbn = {978-0-7619-2303-9},
  keywords = {Business \& Economics / Management,Psychology / Applied Psychology},
  language = {en}
}

@article{brodeur_star_2016,
  title = {Star Wars: The Empirics Strike Back},
  author = {Brodeur, Abel and L{\'e}, Mathias and Sangnier, Marc and Zylberberg, Yanos},
  year = {2016},
  volume = {8},
  pages = {1--32},
  journal = {American Economic Journal: Applied Economics},
  number = {1}
}

@book{burnham_model_2003,
  title = {Model Selection and Multimodel Inference},
  author = {Burnham, Kenneth P and Anderson, David R},
  year = {2003},
  publisher = {{Springer Science \& Business Media}},
  isbn = {0-387-95364-7}
}

@article{casey_reshaping_2012,
  title = {Reshaping Institutions: Evidence on Aid Impacts Using a Preanalysis Plan},
  shorttitle = {Reshaping Institutions},
  author = {Casey, Katherine and Glennerster, Rachel and Miguel, Edward},
  year = {2012},
  month = nov,
  volume = {127},
  pages = {1755--1812},
  issn = {0033-5533},
  doi = {10.1093/qje/qje027},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\4D4UP5PK\\Reshaping-Institutions-Evidence-on-Aid-Impacts.html;C\:\\gl\\Box Sync\\zotero\\storage\\HEP9G4FU\\Reshaping-Institutions-Evidence-on-Aid-Impacts.html;C\:\\gl\\Box Sync\\zotero\\storage\\UZIR9J66\\Reshaping-Institutions-Evidence-on-Aid-Impacts.html},
  journal = {The Quarterly Journal of Economics},
  number = {4}
}

@article{clarke_phantom_2005,
  title = {The Phantom Menace: Omitted Variable Bias in Econometric Research},
  shorttitle = {The Phantom Menace},
  author = {Clarke, Kevin A.},
  year = {2005},
  month = dec,
  volume = {22},
  pages = {341--352},
  issn = {0738-8942},
  doi = {10.1080/07388940500339183},
  abstract = {Quantitative political science is awash in control variables. The justification for these bloated specifications is usually the fear of omitted variable bias. A key underlying assumption is that the danger posed by omitted variable bias can be ameliorated by the inclusion of relevant control variables. Unfortunately, as this article demonstrates, there is nothing in the mathematics of regression analysis that supports this conclusion. The inclusion of additional control variables may increase or decrease the bias, and we cannot know for sure which is the case in any particular situation. A brief discussion of alternative strategies for achieving experimental control follows the main result.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\JMW8UADD\\Clarke - 2005 - The Phantom Menace Omitted Variable Bias in Econo.pdf;C\:\\gl\\Box Sync\\zotero\\storage\\VRHMMMVA\\Clarke - 2005 - The Phantom Menace Omitted Variable Bias in Econo.pdf;C\:\\gl\\Box Sync\\zotero\\storage\\WJX4PH7K\\Clarke - 2005 - The Phantom Menace Omitted Variable Bias in Econo.pdf;C\:\\gl\\Box Sync\\zotero\\storage\\A39AGRQR\\07388940500339183.html;C\:\\gl\\Box Sync\\zotero\\storage\\SCJQHBVT\\07388940500339183.html;C\:\\gl\\Box Sync\\zotero\\storage\\URCFIHG5\\07388940500339183.html},
  journal = {Conflict Management and Peace Science},
  keywords = {control variables,omitted variable bias,research design,specification},
  number = {4}
}

@article{clarke_return_2009,
  title = {Return of the Phantom Menace: Omitted Variable Bias in Political Research},
  shorttitle = {Return of the Phantom Menace},
  author = {Clarke, Kevin A.},
  year = {2009},
  month = feb,
  volume = {26},
  pages = {46--66},
  issn = {0738-8942},
  doi = {10.1177/0738894208097666},
  abstract = {Scholars often assume that the danger posed by omitted variable bias can be ameliorated by the inclusion of large numbers of relevant control variables. However, there is nothing in the mathematics of regression analysis that supports this conclusion. This paper goes beyond textbook treatments of omitted variable bias and shows, both for OLS and for generalized linear models, that the inclusion of additional control variables may increase or decrease the bias, and we cannot know for sure which is the case in any particular situation. The last section of the paper shows how formal sensitivity analysis can be used to determine whether omitted variables are a problem. A substantive example demonstrates the method.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\7MNRZHZ3\\Clarke - 2009 - Return of the Phantom Menace Omitted Variable Bia.pdf},
  journal = {Conflict Management and Peace Science},
  language = {en},
  number = {1}
}

@book{cohen_applied_2003,
  title = {Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences},
  author = {Cohen, Jacob and Cohen, Patricia and West, Stephen G and Aiken, Leona S},
  year = {2003},
  edition = {Third},
  publisher = {{Lawrence Erlbaum Associates}},
  address = {{Mahwah, NJ}}
}

@article{cole_illustrating_2010,
  title = {Illustrating Bias Due to Conditioning on a Collider},
  author = {Cole, Stephen R and Platt, Robert W and Schisterman, Enrique F and Chu, Haitao and Westreich, Daniel and Richardson, David and Poole, Charles},
  year = {2010},
  volume = {39},
  pages = {417--420},
  issn = {1464-3685},
  journal = {International journal of epidemiology},
  keywords = {Epidemiologic Research Design,Humans,Odds Ratio,Regression Analysis,Risk Assessment,Selection Bias},
  number = {2}
}

@article{conger_revised_1974,
  title = {A Revised Definition for Suppressor Variables: {{A}} Guide to Their Identification and Interpretation},
  author = {Conger, Anthony J},
  year = {1974},
  volume = {34},
  pages = {35--46},
  issn = {0013-1644},
  journal = {Educational and psychological measurement},
  number = {1}
}

@article{crede_questionable_2016,
  title = {Questionable Association between Front Boarding and Air Rage},
  author = {Crede, Marcus and Gelman, Andrew and Nickerson, Carol},
  year = {2016},
  month = nov,
  volume = {113},
  pages = {E7348},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1611704113},
  abstract = {DeCelles and Norton (1) conclude that physical inequality (the presence of a first-class cabin) on airplanes is associated with a greater number of air rage incidents in economy class, and that situational inequality (boarding from the front rather than the middle of the airplane) is associated with a greater number of air rage incidents in both economy class and first class. Their study has many flaws that invalidate their conclusions, but we focus on just one, their failure to recognize a statistical artifact in their analyses.

Decelles and Norton's (1) table S2 shows that the correlation between front boarding and economy class incidents equals -0.035 (odds ratio 0.1954), and the correlation between front boarding and first class incidents \ldots{} 

[{$\carriagereturn$}][1]1To whom correspondence should be addressed. Email: mcrede\{at\}iastate.edu.

 [1]: \#xref-corresp-1-1},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\U5KCI92Q\\Crede et al. - 2016 - Questionable association between front boarding an.pdf;C\:\\gl\\Box Sync\\zotero\\storage\\UW3MA6RA\\E7348.html},
  journal = {Proceedings of the National Academy of Sciences},
  language = {en},
  number = {47},
  pmid = {27838612}
}

@article{davenport_policyinduced_2015,
  title = {Policy-Induced Risk and Responsive Participation: The Effect of a Son's Conscription Risk on the Voting Behavior of His Parents},
  shorttitle = {Policy-Induced Risk and Responsive Participation},
  author = {Davenport, Tiffany C.},
  year = {2015},
  month = jan,
  volume = {59},
  pages = {225--241},
  issn = {1540-5907},
  doi = {10.1111/ajps.12117},
  abstract = {When do government policies induce responsive political participation? This study tests two hypotheses in the context of military draft policies. First, policy-induced risk motivates political participation. Second, contextual-level moderators, such as local events that make risk particularly salient, may intensify the effect of risk on participation. I use the random assignment of induction priority in the Vietnam draft lotteries to measure the effect of a son's draft risk on the voter turnout of his parents in the 1972 presidential election. I find higher rates of turnout among parents of men with ``losing'' draft lottery numbers. Among parents from towns with at least one prior war casualty, I find a 7 to 9 percentage point effect of a son's draft risk on his parents' turnout. The local casualty contextual-level moderator is theorized to operate through the mechanism of an availability heuristic, whereby parents from towns with casualties could more readily imagine the adverse consequences of draft risk.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\7DCIGDUC\\Davenport - 2015 - Policy-Induced Risk and Responsive Participation .pdf;C\:\\gl\\Box Sync\\zotero\\storage\\M97BBJCB\\Davenport - 2015 - Policy-Induced Risk and Responsive Participation .pdf;C\:\\gl\\Box Sync\\zotero\\storage\\TFPS34RG\\Davenport - 2015 - Policy-Induced Risk and Responsive Participation .pdf;C\:\\gl\\Box Sync\\zotero\\storage\\GIHMTTG2\\abstract.html;C\:\\gl\\Box Sync\\zotero\\storage\\MKSXMCPW\\abstract.html;C\:\\gl\\Box Sync\\zotero\\storage\\VPPC9KE5\\abstract.html},
  journal = {American Journal of Political Science},
  language = {en},
  number = {1}
}

@article{dewald_replication_1986,
  title = {Replication in Empirical Economics: The Journal of Money, Credit and Banking Project},
  shorttitle = {Replication in Empirical Economics},
  author = {Dewald, William G. and Thursby, Jerry G. and Anderson, Richard G.},
  year = {1986},
  volume = {76},
  pages = {587--603},
  issn = {0002-8282},
  abstract = {This paper examines the role of replication in empirical economic research. It presents the findings of a two-year study that collected programs and data from authors and attempted to replicate their published results. Our research provides new and important information about the extent and causes of failures to replicate published results in economics. Our findings suggest that inadvertent errors in published empirical articles are a commonplace rather thana rare occurrence.},
  journal = {The American Economic Review},
  number = {4}
}

@article{gelbach_when_2016,
  title = {When Do Covariates Matter? {{And}} Which Ones, and How Much?},
  shorttitle = {When Do Covariates Matter?},
  author = {Gelbach, Jonah B.},
  year = {2016},
  month = jan,
  volume = {34},
  pages = {509--543},
  issn = {0734-306X},
  doi = {10.1086/683668},
  abstract = {Authors often add covariates to a base model sequentially either to test a particular coefficient's ``robustness'' or to account for the ``effects'' on this coefficient of adding covariates. This is problematic, due to sequence sensitivity when added covariates are intercorrelated. Using the omitted variables bias formula, I construct a conditional decomposition that accounts for various covariates' role in moving base regressors' coefficients. I also provide a consistent covariance formula. I illustrate this conditional decomposition with NLSY data in an application that exhibits sequence sensitivity. Related extensions include instrumental variables, the fact that my decomposition nests the Oaxaca-Blinder decomposition, and a Hausman test result.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\9EA9GZ85\\683668.html;C\:\\gl\\Box Sync\\zotero\\storage\\HITB2IJG\\683668.html;C\:\\gl\\Box Sync\\zotero\\storage\\MBCB32I9\\683668.html},
  journal = {Journal of Labor Economics},
  number = {2}
}

@article{gelman_statistical_2014,
  title = {The Statistical Crisis in Science Data-Dependent Analysis\textemdash{}a ``garden of Forking Paths''\textemdash{}Explains Why Many Statistically Significant Comparisons Don't Hold Up.},
  author = {Gelman, Andrew and Loken, Eric},
  year = {2014},
  volume = {102},
  pages = {460},
  journal = {American Scientist},
  number = {6}
}

@article{gerber_publication_2010,
  title = {Publication Bias in Two Political Behavior Literatures},
  author = {Gerber, Alan S. and Malhotra, Neil and Dowling, Conor M. and Doherty, David},
  year = {2010},
  month = jul,
  volume = {38},
  pages = {591--613},
  issn = {1532-673X},
  doi = {10.1177/1532673X09350979},
  abstract = {Publication bias occurs when the probability that a paper enters the scholarly literature is a function of the magnitude or significance levels of the coefficient estimates. We investigate publication bias in two large literatures in political behavior: economic voting and the effects of negative advertising. We find that the pattern of published estimates is consistent with the presence of publication bias and that bias is more prevalent in the most influential and highly cited outlets. We consider the possible causes and find some evidence that papers systematically employ one-sided hypothesis tests in response to failure to meet the more demanding critical values associated with two-tailed tests, a practice that leads to misleading reports of the probability of Type I errors.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\4I77RJQ2\\Gerber et al. - 2010 - Publication Bias in Two Political Behavior Literat.pdf;C\:\\gl\\Box Sync\\zotero\\storage\\B56QNA5G\\Gerber et al. - 2010 - Publication Bias in Two Political Behavior Literat.pdf;C\:\\gl\\Box Sync\\zotero\\storage\\CPTPHI9E\\Gerber et al. - 2010 - Publication Bias in Two Political Behavior Literat.pdf},
  journal = {American Politics Research},
  language = {en},
  number = {4}
}

@article{gerber_statistical_2008,
  title = {Do Statistical Reporting Standards Affect What Is Published? {{Publication}} Bias in Two Leading Political Science Journals},
  shorttitle = {Do Statistical Reporting Standards Affect What Is Published?},
  author = {Gerber, Alan and Malhotra, Neil},
  year = {2008},
  month = oct,
  volume = {3},
  pages = {313--326},
  issn = {1554-0626, 1554-0634},
  doi = {10.1561/100.00008024},
  abstract = {Do Statistical Reporting Standards Affect What Is Published? Publication Bias in Two Leading Political Science Journals},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\388DGGEJ\\QJPS-8024.html;C\:\\gl\\Box Sync\\zotero\\storage\\KGQG562X\\QJPS-8024.html;C\:\\gl\\Box Sync\\zotero\\storage\\S9ARUBXP\\QJPS-8024.html},
  journal = {Quarterly Journal of Political Science},
  language = {English},
  number = {3}
}

@article{hainmueller_entropy_2012,
  title = {Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies},
  shorttitle = {Entropy Balancing for Causal Effects},
  author = {Hainmueller, Jens},
  year = {2012},
  month = jan,
  volume = {20},
  pages = {25--46},
  issn = {1047-1987},
  doi = {10.1093/pan/mpr025},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\6A6GRXBA\\1555103.html;C\:\\gl\\Box Sync\\zotero\\storage\\CWE53MCX\\1555103.html;C\:\\gl\\Box Sync\\zotero\\storage\\NGBUK2GU\\1555103.html},
  journal = {Political Analysis},
  number = {1}
}

@article{ho_matching_2007a,
  title = {Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference},
  author = {Ho, Daniel E. and Imai, Kosuke and King, Gary and Stuart, Elizabeth A.},
  year = {2007},
  month = jul,
  volume = {15},
  pages = {199--236},
  issn = {1047-1987},
  doi = {10.1093/pan/mpl013},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\7H6PJ7VW\\Matching-as-Nonparametric-Preprocessing-for.html;C\:\\gl\\Box Sync\\zotero\\storage\\G6QB74SV\\Matching-as-Nonparametric-Preprocessing-for.html;C\:\\gl\\Box Sync\\zotero\\storage\\RU8859WS\\Matching-as-Nonparametric-Preprocessing-for.html},
  journal = {Political Analysis},
  number = {3}
}

@article{holling_suppressor_1983,
  title = {Suppressor Structures in the General Linear Model},
  author = {Holling, Heinz},
  year = {1983},
  volume = {43},
  pages = {1--9},
  issn = {0013-1644},
  journal = {Educational and Psychological Measurement},
  number = {1}
}

@article{horst_role_1941,
  title = {The Role of Predictor Variables Which Are Independent of the Criterion},
  author = {Horst, Paul},
  year = {1941},
  volume = {48},
  pages = {431--436},
  journal = {Social Science Research Council},
  number = {4}
}

@article{humphreys_fishing_2013,
  title = {Fishing, Commitment, and Communication: A Proposal for Comprehensive Nonbinding Research Registration},
  shorttitle = {Fishing, Commitment, and Communication},
  author = {Humphreys, Macartan and {Sanchez de la Sierra}, Raul and {van der Windt}, Peter},
  year = {2013},
  month = jan,
  volume = {21},
  pages = {1--20},
  issn = {1047-1987},
  doi = {10.1093/pan/mps021},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\4RCBN445\\Fishing-Commitment-and-Communication-A-Proposal.html;C\:\\gl\\Box Sync\\zotero\\storage\\52BCBM53\\Fishing-Commitment-and-Communication-A-Proposal.html;C\:\\gl\\Box Sync\\zotero\\storage\\CXEGHQ9T\\Fishing-Commitment-and-Communication-A-Proposal.html},
  journal = {Political Analysis},
  number = {1}
}

@article{imai_covariate_2014,
  title = {Covariate Balancing Propensity Score},
  author = {Imai, Kosuke and Ratkovic, Marc},
  year = {2014},
  month = jan,
  volume = {76},
  pages = {243--263},
  issn = {1467-9868},
  doi = {10.1111/rssb.12027},
  abstract = {The propensity score plays a central role in a variety of causal inference settings. In particular, matching and weighting methods based on the estimated propensity score have become increasingly common in the analysis of observational data. Despite their popularity and theoretical appeal, the main practical difficulty of these methods is that the propensity score must be estimated. Researchers have found that slight misspecification of the propensity score model can result in substantial bias of estimated treatment effects. We introduce covariate balancing propensity score (CBPS) methodology, which models treatment assignment while optimizing the covariate balance. The CBPS exploits the dual characteristics of the propensity score as a covariate balancing score and the conditional probability of treatment assignment. The estimation of the CBPS is done within the generalized method-of-moments or empirical likelihood framework. We find that the CBPS dramatically improves the poor empirical performance of propensity score matching and weighting methods reported in the literature. We also show that the CBPS can be extended to other important settings, including the estimation of the generalized propensity score for non-binary treatments and the generalization of experimental estimates to a target population. Open source software is available for implementing the methods proposed.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\4G3A3UIJ\\scholar.enw;C\:\\gl\\Box Sync\\zotero\\storage\\6K6Q3E5Q\\scholar.enw;C\:\\gl\\Box Sync\\zotero\\storage\\9XZHQ8KB\\scholar.enw;C\:\\gl\\Box Sync\\zotero\\storage\\4SABU3XT\\abstract.html;C\:\\gl\\Box Sync\\zotero\\storage\\CBWMHMQ5\\abstract.html;C\:\\gl\\Box Sync\\zotero\\storage\\ZNINAQK5\\abstract.html},
  journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  keywords = {Causal inference,Instrumental variables,Inverse propensity score weighting,Marginal structural models,Observational studies,Propensity score matching,Randomized experiments},
  language = {en},
  number = {1}
}

@article{ioannidis_power_2017,
  title = {The Power of Bias in Economics Research},
  author = {Ioannidis, John PA and Stanley, Tom D and Doucouliagos, Hristos},
  year = {2017},
  volume = {127},
  pages = {F236--F265},
  issn = {0013-0133},
  journal = {The Economic Journal}
}

@article{ioannidis_why_2005,
  title = {Why Most Published Research Findings Are False},
  author = {Ioannidis, John PA},
  year = {2005},
  volume = {2},
  pages = {e124},
  issn = {1549-1676},
  journal = {PLoS Medicine},
  number = {8}
}

@article{kim_causal_2019,
  title = {The Causal Structure of Suppressor Variables},
  author = {Kim, Yongnam},
  year = {2019},
  month = feb,
  pages = {1076998619825679},
  issn = {1076-9986},
  doi = {10.3102/1076998619825679},
  abstract = {Suppression effects in multiple linear regression are one of the most elusive phenomena in the educational and psychological measurement literature. The question is, How can including a variable, which is completely unrelated to the criterion variable, in regression models significantly increase the predictive power of the regression models? In this article, we view suppression from a causal perspective and uncover the causal structure of suppressor variables. Using causal discovery algorithms, we show that classical suppressors defined by Horst (1941) are generated from causal structures which reveal the equivalence between suppressors and instrumental variables. Although the educational and psychological measurement literature has long recommended that researchers include suppressors in regression models, the causal inference literature has recently recommended that researchers exclude instrumental variables. The conflicting views between the two disciplines can be resolved by considering the different purposes of statistical models, prediction and causal explanation.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\BN5VAAB2\\Kim - 2019 - The Causal Structure of Suppressor Variables.pdf},
  journal = {Journal of Educational and Behavioral Statistics},
  language = {en}
}

@article{king_replication_1995,
  title = {Replication, Replication},
  author = {King, Gary},
  year = {1995},
  volume = {28},
  pages = {444--452},
  issn = {1049-0965},
  doi = {10.2307/420301},
  journal = {PS: Political Science and Politics},
  number = {3}
}

@article{klein_investigating_2014,
  title = {Investigating Variation in Replicability},
  author = {Klein, Richard A and Ratliff, Kate A and Vianello, Michelangelo and Adams Jr, Reginald B and Bahn{\'i}k, {\v S}t{\v e}p{\'a}n and Bernstein, Michael J and Bocian, Konrad and Brandt, Mark J and Brooks, Beach and Brumbaugh, Claudia Chloe},
  year = {2014},
  volume = {14},
  pages = {142--52},
  journal = {Social psychology},
  number = {3}
}

@article{klein_many_2018,
  title = {Many {{Labs}} 2: {{Investigating}} Variation in Replicability across Samples and Settings},
  author = {Klein, Richard A and Vianello, Michelangelo and Hasselman, Fred and Adams, Byron G and Adams Jr, Reginald B and Alper, Sinan and Aveyard, Mark and Axt, Jordan R and Babalola, Mayowa T and Bahn{\'i}k, {\v S}t{\v e}p{\'a}n},
  year = {2018},
  volume = {1},
  pages = {443--490},
  issn = {2515-2459},
  journal = {Advances in Methods and Practices in Psychological Science},
  number = {4}
}

@article{leamer_let_1983,
  title = {Let's Take the Con out of Econometrics},
  author = {Leamer, Edward E.},
  year = {1983},
  volume = {73},
  pages = {31--43},
  issn = {00028282},
  doi = {10.2307/1803924},
  journal = {The American Economic Review}
}

@article{leamer_sensitivity_1985,
  title = {Sensitivity Analyses Would Help},
  author = {Leamer, Edward E.},
  year = {1985},
  volume = {75},
  pages = {308--313},
  issn = {0002-8282},
  journal = {The American Economic Review}
}

@article{leamer_svalues_2016,
  title = {S-Values: Conventional Context-Minimal Measures of the Sturdiness of Regression Coefficients},
  shorttitle = {-{{Values}}},
  author = {Leamer, Edward E.},
  year = {2016},
  month = jul,
  volume = {193},
  pages = {147--161},
  issn = {0304-4076},
  doi = {10.1016/j.jeconom.2015.10.013},
  abstract = {This paper proposes a context-minimal range of alternative regression models that is used to generate a range of alternative estimates. A prior distribution is assumed with a zero mean but an ambiguous covariance matrix. The choice of the prior covariance matrix is facilitated by transformation to standardized variables which makes the prior expected R 2 equal to the sum of the prior variances. Three different ranges of the prior expected R 2 are used to define three different intervals of prior covariance matrices which are used to produce three different sets of s -values.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\979KITJJ\\S0304407616300185.html;C\:\\gl\\Box Sync\\zotero\\storage\\FRIBGEDH\\S0304407616300185.html;C\:\\gl\\Box Sync\\zotero\\storage\\V7TJVTTI\\S0304407616300185.html},
  journal = {Journal of Econometrics},
  keywords = {Model ambiguity,Regression,REGRESSION,s  -values,s -values},
  number = {1}
}

@article{lewis_suppression_1986,
  title = {Suppression and Enhancement in Bivariate Regression},
  author = {Lewis, Jerry W and Escobar, Luis A},
  year = {1986},
  volume = {35},
  pages = {17--26},
  issn = {0039-0526},
  journal = {Journal of the Royal Statistical Society: Series D (The Statistician)},
  number = {1}
}

@article{lin_agnostic_2013,
  title = {Agnostic Notes on Regression Adjustments to Experimental Data: Reexamining Freedman's Critique},
  shorttitle = {Agnostic Notes on Regression Adjustments to Experimental Data},
  author = {Lin, Winston},
  year = {2013},
  volume = {7},
  pages = {295--318},
  issn = {1932-6157},
  abstract = {Freedman [Adv. in Appl. Math. 40 (2008) 180\textemdash{}193; Ann. Appl. Stat. 2 (2008) 176\textemdash{}196] critiqued ordinary least squares regression adjustment of estimated treatment effects in randomized experiments, using Neyman's model for randomization inference. Contrary to conventional wisdom, he argued that adjustment can lead to worsened asymptotic precision, invalid measures of precision, and small-sample bias. This paper shows that in sufficiently large samples, those problems are either minor or easily fixed. OLS adjustment cannot hurt asymptotic precision when a full set of treatment\textemdash{}covariate interactions is included. Asymptotically valid confidence intervals can be constructed with the Huber\textemdash{}White sandwich standard error estimator. Checks on the asymptotic approximations are illustrated with data from Angrist, Lang, and Oreopoulos's [Am. Econ. J.: Appl. Econ. 1:1 (2009) 136\textemdash{}163] evaluation of strategies to improve college students' achievement. The strongest reasons to support Freedman's preference for unadjusted estimates are transparency and the dangers of specification search.},
  journal = {The Annals of Applied Statistics},
  number = {1}
}

@article{loken_measurement_2017,
  title = {Measurement Error and the Replication Crisis},
  author = {Loken, Eric and Gelman, Andrew},
  year = {2017},
  volume = {355},
  pages = {584--585},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\D7EV5HJS\\Loken and Gelman - 2017 - Measurement error and the replication crisis.pdf;C\:\\gl\\Box Sync\\zotero\\storage\\GBEE5ASD\\Loken and Gelman - 2017 - Measurement error and the replication crisis.pdf;C\:\\gl\\Box Sync\\zotero\\storage\\QHTISAHE\\Loken and Gelman - 2017 - Measurement error and the replication crisis.pdf;C\:\\gl\\Box Sync\\zotero\\storage\\92UI8EFR\\584.html;C\:\\gl\\Box Sync\\zotero\\storage\\ANSFW8WH\\584.html;C\:\\gl\\Box Sync\\zotero\\storage\\TWGWNEC2\\584.html},
  journal = {Science},
  number = {6325}
}

@article{lynam_perils_2006,
  title = {The Perils of Partialling: Cautionary Tales from Aggression and Psychopathy},
  shorttitle = {The Perils of Partialling},
  author = {Lynam, Donald R. and Hoyle, Rick H. and Newman, Joseph P.},
  year = {2006},
  month = sep,
  volume = {13},
  pages = {328--341},
  issn = {1073-1911},
  doi = {10.1177/1073191106290562},
  abstract = {Although a powerful technique, the partialling of independent variables from one another in the context of multiple regression analysis poses certain perils. The present article argues that the most important and underappreciated peril is the difficulty in knowing what construct an independent variable represents once the variance shared with other independent variables is removed. The present article presents illustrative analyses in a large sample of inmates (n =696) using three measures from the psychopathy and aggression fields. Results indicate that in terms of relations among items on a single scale and relations between scales, the raw and residualized scores bore little resemblance to one another. It is argued that researchers must decide to which construct\textemdash{}the one represented by the original scale or the one represented by the residualized scale\textemdash{}conclusions are meant to apply. Difficulties in applying the conclusions to the residualized scale are highlighted and best practices suggested.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\KKZN4BSR\\Lynam et al. - 2006 - The Perils of Partialling Cautionary Tales from A.pdf},
  journal = {Assessment},
  language = {en},
  number = {3}
}

@article{maassen_suppressor_2001,
  title = {Suppressor Variables in Path Models: Definitions and Interpretations},
  shorttitle = {Suppressor Variables in Path Models},
  author = {Maassen, Gerard D. and Bakker, Arnold B.},
  year = {2001},
  month = nov,
  volume = {30},
  pages = {241--270},
  issn = {0049-1241},
  doi = {10.1177/0049124101030002004},
  abstract = {Suppressor variables are well known in the context of multiple regression analysis. Using several examples, the authors demonstrate that the different forms of the suppressor phenomenon described in the literature occur not only in prediction equations but also in the explanatory use of multiple regression, including structural equations models. Moreover, they show that the probability of their occurrence is relatively high in models with latent variables, in which the suppressed variable is corrected for measurement errors. Special attention will be paid to the two-wave model since this is particularly liable to the suppressor phenomenon. The occurrence of suppression in structural equations models is usually not foreseen and confronts researchers with problems of interpretation. The authors discuss definitions of the suppressor phenomenon, show how the unwary researcher can be warned against it, and present guidelines for the interpretation of the results.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\ZJPY9IMA\\MAASSEN and BAKKER - 2001 - Suppressor Variables in Path Models Definitions a.pdf},
  journal = {Sociological Methods \& Research},
  language = {en},
  number = {2}
}

@article{mackinnon_equivalence_2000,
  title = {Equivalence of the Mediation, Confounding and Suppression Effect},
  author = {MacKinnon, David P and Krull, Jennifer L and Lockwood, Chondra M},
  year = {2000},
  volume = {1},
  pages = {173--181},
  issn = {1389-4986},
  journal = {Prevention Science},
  number = {4}
}

@article{malesky_monopoly_2015,
  title = {Monopoly Money: Foreign Investment and Bribery in {{Vietnam}}, a Survey Experiment},
  shorttitle = {Monopoly Money},
  author = {Malesky, Edmund J. and Gueorguiev, Dimitar D. and Jensen, Nathan M.},
  year = {2015},
  month = feb,
  volume = {59},
  pages = {419--439},
  issn = {1540-5907},
  doi = {10.1111/ajps.12126},
  abstract = {Prevailing work argues that foreign investment reduces corruption, either by competing down monopoly rents or diffusing best practices of corporate governance. We argue that the mechanisms generating this relationship are not clear because the extant empirical work is too heavily drawn from aggregations of total foreign investment entering an economy. Alternatively, we suggest that openness to foreign investment has differential effects on corruption even within the same country and under the same domestic institutions over time. We argue that foreign firms use bribes to enter protected industries in search of rents, and therefore we expect variation in bribe propensity across sectors according to expected profitability. We test this effect using a list experiment embedded in three waves of a nationally representative survey of 20,000 foreign and domestic businesses in Vietnam, finding that the effect of economic openness on the probability to engage in bribes is conditional on policies that restrict investment.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\V6RGIIVT\\abstract.html},
  journal = {American Journal of Political Science},
  language = {en},
  number = {2}
}

@article{mcshane_abandon_2019,
  title = {Abandon Statistical Significance},
  author = {McShane, Blakeley B and Gal, David and Gelman, Andrew and Robert, Christian and Tackett, Jennifer L},
  year = {2019},
  volume = {73},
  pages = {235--245},
  issn = {0003-1305},
  journal = {The American Statistician},
  number = {sup1}
}

@article{middleton_bias_2016,
  title = {Bias Amplification and Bias Unmasking},
  author = {Middleton, Joel A and Scott, Marc A and Diakow, Ronli and Hill, Jennifer L},
  year = {2016},
  volume = {24},
  pages = {307--323},
  issn = {1476-4989},
  journal = {Political Analysis},
  number = {3}
}

@article{miguel_promoting_2014,
  title = {Promoting Transparency in Social Science Research},
  author = {Miguel, E. and Camerer, C. and Casey, K. and Cohen, J. and Esterling, K. M. and Gerber, A. and Glennerster, R. and Green, D. P. and Humphreys, M. and Imbens, G. and Laitin, D. and Madon, T. and Nelson, L. and Nosek, B. A. and Petersen, M. and Sedlmayr, R. and Simmons, J. P. and Simonsohn, U. and {der Laan}, M. Van},
  year = {2014},
  month = jan,
  volume = {343},
  pages = {30--31},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.1245317},
  abstract = {There is growing appreciation for the advantages of experimentation in the social sciences. Policy-relevant claims that in the past were backed by theoretical arguments and inconclusive correlations are now being investigated using more credible methods. Changes have been particularly pronounced in development economics, where hundreds of randomized trials have been carried out over the last decade. When experimentation is difficult or impossible, researchers are using quasi-experimental designs. Governments and advocacy groups display a growing appetite for evidence-based policy-making. In 2005, Mexico established an independent government agency to rigorously evaluate social programs, and in 2012, the U.S. Office of Management and Budget advised federal agencies to present evidence from randomized program evaluations in budget requests (1, 2). Social scientists should adopt higher transparency standards to improve the quality and credibility of research. Social scientists should adopt higher transparency standards to improve the quality and credibility of research.},
  copyright = {Copyright \textcopyright{} 2014, American Association for the Advancement of Science},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\EZGVPXIZ\\Miguel et al. - 2014 - Promoting Transparency in Social Science Research.pdf;C\:\\gl\\Box Sync\\zotero\\storage\\I2BBJJQA\\Miguel et al. - 2014 - Promoting Transparency in Social Science Research.pdf;C\:\\gl\\Box Sync\\zotero\\storage\\P8CA2DDS\\Miguel et al. - 2014 - Promoting Transparency in Social Science Research.pdf;C\:\\gl\\Box Sync\\zotero\\storage\\2MSKT53E\\30.html;C\:\\gl\\Box Sync\\zotero\\storage\\IUD7ZH58\\30.html;C\:\\gl\\Box Sync\\zotero\\storage\\W7KUWZMP\\30.html},
  journal = {Science},
  language = {en},
  number = {6166},
  pmid = {24385620}
}

@article{montgomery_bayesian_2010,
  title = {Bayesian Model Averaging: Theoretical Developments and Practical Applications},
  shorttitle = {Bayesian Model Averaging},
  author = {Montgomery, Jacob M. and Nyhan, Brendan},
  year = {2010},
  month = apr,
  volume = {18},
  pages = {245--270},
  issn = {1047-1987, 1476-4989},
  doi = {10.1093/pan/mpq001},
  abstract = {Political science researchers typically conduct an idiosyncratic search of possible model configurations and then present a single specification to readers. This approach systematically understates the uncertainty of our results, generates fragile model specifications, and leads to the estimation of bloated models with too many control variables. Bayesian model averaging (BMA) offers a systematic method for analyzing specification uncertainty and checking the robustness of one's results to alternative model specifications, but it has not come into wide usage within the discipline. In this paper, we introduce important recent developments in BMA and show how they enable a different approach to using the technique in applied social science research. We illustrate the methodology by reanalyzing data from three recent studies using BMA software we have modified to respect statistical conventions within political science.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\2NPQ6Z2P\\3179D92A3C9353DE7E4674987C33FD28.html;C\:\\gl\\Box Sync\\zotero\\storage\\NFQFGG4S\\3179D92A3C9353DE7E4674987C33FD28.html;C\:\\gl\\Box Sync\\zotero\\storage\\SDRR8HNU\\3179D92A3C9353DE7E4674987C33FD28.html},
  journal = {Political Analysis},
  number = {2}
}

@article{moore_driving_2013,
  title = {Driving Support: Workers, {{PACs}}, and Congressional Support of the Auto Industry},
  shorttitle = {Driving Support},
  author = {Moore, Ryan T. and Powell, Eleanor Neff and Reeves, Andrew},
  year = {2013},
  volume = {15},
  pages = {137--162},
  issn = {1469-3569},
  doi = {10.1515/bap-2013-0005},
  abstract = {In 2008 and 2009, the House of Representatives directed billions of dollars to the auto industry by passing a bailout and the ``cash for clunkers'' program. Moving beyond corporate influence via campaign contributions, we demonstrate that the presence of auto workers in a district strongly predicts legislative support for both bills. In addition to this critical legislation, we also analyze over 250 bills on which the auto industry either lobbied or took a public position. We find no patterns relating a district's workers or corporate campaign contributions to these votes on broader legislation where other groups, such as environmental advocates or labor unions, are at the table. Instead, the auto industry garners consistent support only on quasi-private, particularistic legislation. Thus, we contend that on particularistic legislation the presence of workers (not just campaign contributions) drives legislative support; however, when legislators expand the scope of conflict, the influence of a single industry is attentuated by other interests.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\3VBBEKGW\\Moore et al. - 2013 - Driving support workers, PACs, and congressional .pdf;C\:\\gl\\Box Sync\\zotero\\storage\\ARK6VUDG\\Moore et al. - 2013 - Driving support workers, PACs, and congressional .pdf;C\:\\gl\\Box Sync\\zotero\\storage\\QQ759II8\\Moore et al. - 2013 - Driving support workers, PACs, and congressional .pdf},
  journal = {Business and Politics},
  number = {2}
}

@article{nickerson_simpson_2019,
  title = {Simpson's {{Paradox}} Is Suppression, but {{Lord}}'s {{Paradox}} Is Neither: Clarification of and Correction to {{Tu}}, {{Gunnell}}, and {{Gilthorpe}} (2008)},
  author = {Nickerson, Carol A and Brown, Nicholas JL},
  year = {2019},
  volume = {16},
  pages = {5--16},
  issn = {1742-7622},
  journal = {Emerging Themes in Epidemiology},
  number = {1}
}

@unpublished{ning_high_2017,
  title = {High Dimensional Propensity Score Estimation via Covariate Balancing},
  author = {Ning, Yang and Peng, Sida and Imai, Kosuke},
  year = {2017},
  address = {{Harvard University}},
  note = {https://imai.fas.harvard.edu/research/files/hdCBPS.pdf}
}

@article{oster_unobservable_2016,
  title = {Unobservable Selection and Coefficient Stability: Theory and Evidence},
  shorttitle = {Unobservable Selection and Coefficient Stability},
  author = {Oster, Emily},
  year = {2016},
  month = sep,
  pages = {1--18},
  issn = {0735-0015},
  doi = {10.1080/07350015.2016.1227711},
  abstract = {A common approach to evaluating robustness to omitted variable bias is to observe coefficient movements after inclusion of controls. This is informative only if selection on observables is informative about selection on unobservables. Although this link is known in theory in existing literature, very few empirical articles approach this formally. I develop an extension of the theory that connects bias explicitly to coefficient stability. I show that it is necessary to take into account coefficient and R-squared movements. I develop a formal bounding argument. I show two validation exercises and discuss application to the economics literature. Supplementary materials for this article are available online.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\38PFZD73\\07350015.2016.html;C\:\\gl\\Box Sync\\zotero\\storage\\AI554J3F\\07350015.2016.html;C\:\\gl\\Box Sync\\zotero\\storage\\EAZI2TGN\\07350015.2016.html},
  journal = {Journal of Business \& Economic Statistics}
}

@article{pandey_suppressor_2010,
  title = {Suppressor Variables in Social Work Research: Ways to Identify in Multiple Regression Models},
  shorttitle = {Suppressor Variables in Social Work Research},
  author = {Pandey, Shanta and Elliott, William},
  year = {2010},
  month = jan,
  volume = {1},
  pages = {28--40},
  issn = {2334-2315},
  doi = {10.5243/jsswr.2010.2},
  abstract = {Suppressor variables may be more common in social work research than what is currently recognized. We review different types of suppressor variables and illustrate systematic ways to identify them in multiple regression using four statistics: R2, sum of squares, regression weight, and comparing zero-order correlations with respective semipartial correlations.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\BZ4NB4ZH\\Pandey and Elliott - 2010 - Suppressor Variables in Social Work Research Ways.pdf;C\:\\gl\\Box Sync\\zotero\\storage\\HT4R3AWP\\jsswr.2010.html},
  journal = {Journal of the Society for Social Work and Research},
  number = {1}
}

@article{paulhus_two_2004,
  title = {Two Replicable Suppressor Situations in Personality Research},
  author = {Paulhus, Delroy L. and Robins, Richard W. and Trzesniewski, Kali H. and Tracy, Jessica L.},
  year = {2004},
  volume = {39},
  pages = {303--328},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\AF3ED855\\Paulhus et al. - 2004 - Two replicable suppressor situations in personalit.pdf;C\:\\gl\\Box Sync\\zotero\\storage\\WFZZAR8J\\s15327906mbr3902_7.html},
  journal = {Multivariate Behavioral Research},
  number = {2}
}

@book{pearl_causality_2009,
  title = {Causality},
  author = {Pearl, Judea},
  year = {2009},
  publisher = {{Cambridge University Press}},
  address = {{Cambridge, NY}},
  isbn = {0-521-89560-X}
}

@inproceedings{pearl_class_2010,
  title = {On a Class of Bias-Amplifying Variables That Endanger Effect Estimates},
  booktitle = {Proceedings of the {{Twenty}}-{{Sixth Conference}} on {{Uncertainty}} in {{ARtificial Intelligence}}},
  author = {Pearl, Judea},
  year = {2010},
  pages = {417--424},
  publisher = {{AUAI}},
  address = {{Corvallis, OR}}
}

@article{pearl_invited_2011,
  title = {Invited Commentary: Understanding Bias Amplification},
  author = {Pearl, Judea},
  year = {2011},
  volume = {174},
  pages = {1223--1227},
  issn = {1476-6256},
  journal = {American Journal of Epidemiology},
  number = {11}
}

@techreport{pei_poorly_2017,
  title = {Poorly Measured Confounders Are More Useful on the Left than on the Right},
  author = {Pei, Zhuan and Pischke, J{\"o}rn-Steffen and Schwandt, Hannes},
  year = {2017},
  month = mar,
  institution = {{National Bureau of Economic Research}},
  abstract = {Researchers frequently test identifying assumptions in regression based research designs (which include instrumental variables or difference-in-differences models) by adding additional control variables on the right hand side of the regression. If such additions do not affect the coefficient of interest (much) a study is presumed to be reliable. We caution that such invariance may result from the fact that the observed variables used in such robustness checks are often poor measures of the potential underlying confounders. In this case, a more powerful test of the identifying assumption is to put the variable on the left hand side of the candidate regression. We provide derivations for the estimators and test statistics involved, as well as power calculations, which can help applied researchers interpret their findings. We illustrate these results in the context of various strategies which have been suggested to identify the returns to schooling.},
  type = {Working {{Paper}}}
}

@article{RSSB:RSSB12167,
  title = {Causal Inference by Using Invariant Prediction: Identification and Confidence Intervals},
  author = {Peters, Jonas and B{\"u}hlmann, Peter and Meinshausen, Nicolai},
  year = {2016},
  volume = {78},
  pages = {947--1012},
  issn = {1467-9868},
  doi = {10.1111/rssb.12167},
  journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  keywords = {Causal discovery,Causal inference,Confidence intervals,Invariant prediction},
  number = {5}
}

@article{sekhon_multivariate_2011,
  title = {Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for r},
  shorttitle = {Multivariate and Propensity Score Matching Software with Automated Balance Optimization},
  author = {Sekhon, Jasjeet S.},
  year = {2011},
  volume = {42},
  abstract = {Matching is an R package which provides functions for multivariate and propensity score matching and for finding optimal covariate balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance actually has been obtained are provided. The underlying matching algorithm is written in C++, makes extensive use of system BLAS and scales efficiently with dataset size. The genetic algorithm which finds optimal balance is parallelized and can make use of multiple CPUs or a cluster of computers. A large number of options are provided which control exactly how the matching is conducted and how balance is evaluated.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\C95ZWCKQ\\summary.html;C\:\\gl\\Box Sync\\zotero\\storage\\GGZINQIN\\summary.html;C\:\\gl\\Box Sync\\zotero\\storage\\ITSCQ6NG\\summary.html},
  journal = {Journal of Statistical Software}
}

@article{simmons_falsepositive_2011,
  title = {False-Positive Psychology},
  author = {Simmons, Joseph P. and Nelson, Leif D. and Simonsohn, Uri},
  year = {2011},
  month = nov,
  volume = {22},
  pages = {1359--1366},
  doi = {10.1177/0956797611417632},
  abstract = {In this article, we accomplish two things. First, we show that despite empirical psychologists' nominal endorsement of a low rate of false-positive findings ({$\leq$} .05), flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates. In many cases, a researcher is more likely to falsely find evidence that an effect exists than to correctly find evidence that it does not. We present computer simulations and a pair of actual experiments that demonstrate how unacceptably easy it is to accumulate (and report) statistically significant evidence for a false hypothesis. Second, we suggest a simple, low-cost, and straightforwardly effective disclosure-based solution to this problem. The solution involves six concrete requirements for authors and four guidelines for reviewers, all of which impose a minimal burden on the publication process.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\WSWZDW75\\scholar.enw;C\:\\gl\\Box Sync\\zotero\\storage\\X9NAD6PR\\scholar.enw;C\:\\gl\\Box Sync\\zotero\\storage\\ZF8BMH6P\\scholar.enw},
  journal = {Psychological Science}
}

@techreport{simonsohn_specification_2015,
  title = {Specification Curve: Descriptive and Inferential Statistics on All Reasonable Specifications},
  shorttitle = {Specification Curve},
  author = {Simonsohn, Uri and Simmons, Joseph P. and Nelson, Leif D.},
  year = {2015},
  month = nov,
  address = {{Rochester, NY}},
  institution = {{Social Science Research Network}},
  abstract = {Empirical results often hinge on data analytic decisions that are simultaneously defensible, arbitrary, and motivated. To mitigate this problem we introduce Spe},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\CXSTWD6T\\papers.html;C\:\\gl\\Box Sync\\zotero\\storage\\GA87NDMX\\papers.html;C\:\\gl\\Box Sync\\zotero\\storage\\P4KRIDX3\\papers.html},
  keywords = {p-hacking,Specification Curve},
  number = {2694998},
  type = {{{SSRN Scholarly Paper}}}
}

@article{steiner_mechanics_2016,
  title = {The Mechanics of Omitted Variable Bias: Bias Amplification and Cancellation of Offsetting Biases},
  shorttitle = {The Mechanics of Omitted Variable Bias},
  author = {Steiner, Peter M. and Kim, Yongnam},
  year = {2016},
  volume = {4},
  issn = {2193-3677},
  doi = {10.1515/jci-2016-0009},
  abstract = {Causal inference with observational data frequently requires researchers to estimate treatment effects conditional on a set of observed covariates, hoping that they remove or at least reduce the confounding bias. Using a simple linear (regression) setting with two confounders \textendash{} one observed (X), the other unobserved (U) \textendash{} we demonstrate that conditioning on the observed confounder X does not necessarily imply that the confounding bias decreases, even if X is highly correlated with U. That is, adjusting for X may increase instead of reduce the omitted variable bias (OVB). Two phenomena can cause an increasing OVB: (i) bias amplification and (ii) cancellation of offsetting biases. Bias amplification occurs because conditioning on X amplifies any remaining bias due to the omitted confounder U. Cancellation of offsetting biases is an issue whenever X and U induce biases in opposite directions such that they perfectly or partially offset each other, in which case adjusting for X inadvertently cancels the bias-offsetting effect. In this article we discuss the conditions under which adjusting for X increases OVB, and demonstrate that conditioning on X increases the imbalance in U, which turns U into an even stronger confounder. We also show that conditioning on an unreliably measured confounder can remove more bias than the corresponding reliable measure. Practical implications for causal inference will be discussed.},
  file = {C\:\\gl\\Box Sync\\zotero\\storage\\QMPHLGKA\\Steiner and Kim - 2016 - The Mechanics of Omitted Variable Bias Bias Ampli.pdf},
  journal = {Journal of Causal Inference},
  keywords = {bias amplification,causal inference,measurement error,offsetting bias,Omitted variable bias},
  number = {2}
}

@book{tesler_obama_2010,
  title = {Obama's Race: The 2008 Election and the Dream of a Post-Racial America},
  author = {Tesler, Michael and Sears, David O},
  year = {2010},
  publisher = {{University of Chicago Press}},
  address = {{Chicago}},
  isbn = {0-226-79383-4}
}

@book{theil_principles_1971,
  title = {Principles of Econometrics},
  author = {Theil, Henri},
  year = {1971},
  publisher = {{Wiley}},
  address = {{New York, NY}}
}

@article{tzelgov_suppression_1991,
  title = {Suppression Situations in Psychological Research: {{Definitions}}, Implications, and Applications.},
  author = {Tzelgov, Joseph and Henik, Avishai},
  year = {1991},
  volume = {109},
  pages = {524},
  issn = {1939-1455},
  journal = {Psychological Bulletin},
  number = {3}
}

@article{wooldridge_should_2016,
  title = {Should Instrumental Variables Be Used as Matching Variables?},
  author = {Wooldridge, Jeffrey M},
  year = {2016},
  volume = {70},
  pages = {232--237},
  issn = {1090-9443},
  journal = {Research in Economics},
  number = {2}
}

@article{wyss_reducing_2014,
  title = {Reducing Bias Amplification in the Presence of Unmeasured Confounding through Out-of-Sample Estimation Strategies for the Disease Risk Score},
  author = {Wyss, Richard and Lunt, Mark and Brookhart, M Alan and Glynn, Robert J and St{\"u}rmer, Til},
  year = {2014},
  volume = {2},
  pages = {131--146},
  issn = {2193-3685},
  journal = {Journal of Causal Inference},
  number = {2}
}


